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Article

Cross-relational clustering with user's guidance

Authors:

Philip S. YuAuthors Info & Claims

KDD '05: Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining

Pages 344 - 353

https://doi.org/10.1145/1081870.1081910

Published: 21 August 2005 Publication History

Abstract

Clustering is an essential data mining task with numerous applications. However, data in most real-life applications are high-dimensional in nature, and the related information often spreads across multiple relations. To ensure effective and efficient high-dimensional, cross-relational clustering, we propose a new approach, called CrossClus, which performs cross-relational clustering with user's guidance. We believe that user's guidance, even likely in very simple forms, could be essential for effective high-dimensional clustering since a user knows well the application requirements and data semantics. CrossClus is carried out as follows: A user specifies a clustering task and selects one or a small set of features pertinent to the task. CrossClus extracts the set of highly relevant features in multiple relations connected via linkages defined in the database schema, evaluates their effectiveness based on user's guidance, and identifies interesting clusters that fit user's needs. This method takes care of both quality in feature extraction and efficiency in clustering. Our comprehensive experiments demonstrate the effectiveness and scalability of this approach.

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Cited By

Huang Y(2021)Incorporating domain ontology information into clustering in heterogeneous networksWIREs Data Mining and Knowledge Discovery10.1002/widm.141311:4Online publication date: 10-May-2021
https://doi.org/10.1002/widm.1413
Chen XRuan CZhang YChen H(2019)Heterogeneous information network based clustering for precision traditional Chinese medicineBMC Medical Informatics and Decision Making10.1186/s12911-019-0963-019:S6Online publication date: 19-Dec-2019
https://doi.org/10.1186/s12911-019-0963-0
Huang Y(2017)Clustering multi-typed objects in extended star-structured heterogeneous dataIntelligent Data Analysis10.3233/IDA-15041621:2(225-241)Online publication date: 2-Mar-2017
https://doi.org/10.3233/IDA-150416
Show More Cited By

Index Terms

Cross-relational clustering with user's guidance
1. Information systems
  1. Information systems applications
    1. Data mining

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Information

Published In

cover image ACM Conferences

KDD '05: Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining

August 2005

844 pages

ISBN:159593135X

DOI:10.1145/1081870

General Chair:
Robert Grossman
University of Illinois at Chicago & Open Data Partners, USA
,
Program Chairs:
Roberto Bayardo
IBM Almaden Research, USA
,
Kristin Bennett
RPI, USA

Copyright © 2005 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 August 2005

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KDD05

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KDD05: The Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 21 - 24, 2005

Illinois, Chicago, USA

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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KDD '25

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The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 3 - 7, 2025

Toronto , ON , Canada

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Cited By

Huang Y(2021)Incorporating domain ontology information into clustering in heterogeneous networksWIREs Data Mining and Knowledge Discovery10.1002/widm.141311:4Online publication date: 10-May-2021
https://doi.org/10.1002/widm.1413
Chen XRuan CZhang YChen H(2019)Heterogeneous information network based clustering for precision traditional Chinese medicineBMC Medical Informatics and Decision Making10.1186/s12911-019-0963-019:S6Online publication date: 19-Dec-2019
https://doi.org/10.1186/s12911-019-0963-0
Huang Y(2017)Clustering multi-typed objects in extended star-structured heterogeneous dataIntelligent Data Analysis10.3233/IDA-15041621:2(225-241)Online publication date: 2-Mar-2017
https://doi.org/10.3233/IDA-150416
Guo TWu JZhu XZhang C(2017)Combining Structured Node Content and Topology Information for Networked Graph ClusteringACM Transactions on Knowledge Discovery from Data10.1145/299619711:3(1-29)Online publication date: 21-Mar-2017
https://dl.acm.org/doi/10.1145/2996197
Cao JWang SWang H(2017)Detecting Communities on Topic of Transportation With Sparse Crowd AnnotationsIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2016.259632118:4(1017-1022)Online publication date: 1-Apr-2017
https://dl.acm.org/doi/10.1109/TITS.2016.2596321
Chen CTong HPrakash BEliassi-Rad TFaloutsos MFaloutsos C(2016)Eigen-Optimization on Large Graphs by Edge ManipulationACM Transactions on Knowledge Discovery from Data10.1145/290314810:4(1-30)Online publication date: 14-Jun-2016
https://dl.acm.org/doi/10.1145/2903148
Cao JWang SQiao FWang HWang FYu P(2016)User-Guided Large Attributed Graph Clustering with Multiple Sparse AnnotationsAdvances in Knowledge Discovery and Data Mining10.1007/978-3-319-31753-3_11(127-138)Online publication date: 12-Apr-2016
https://doi.org/10.1007/978-3-319-31753-3_11
Huang YGao X(2014)Clustering on heterogeneous networksWiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery10.1002/widm.11264:3(213-233)Online publication date: 1-May-2014
https://dl.acm.org/doi/10.1002/widm.1126
Jiménez ABerzal FCubero J(2012)Using trees to mine multirelational databasesData Mining and Knowledge Discovery10.1007/s10618-011-0218-x24:1(1-39)Online publication date: 1-Jan-2012
https://dl.acm.org/doi/10.1007/s10618-011-0218-x
Xavier-Júnior JCanuto AGonçalves Lde Oliveira L(2012)New approach for clustering relational data based on relationship and attribute informationProceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II10.1007/978-3-642-33266-1_56(451-458)Online publication date: 11-Sep-2012
https://dl.acm.org/doi/10.1007/978-3-642-33266-1_56
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